Spatiotemporal patterns and drivers of land degradation and development in China's terrestrial ecosystems from 2000 to 2020

ABSTRACT : Land degradation and development (LDD) result from the combined driving force of natural constraints and human activities on the productivity and stability of terrestrial ecosystems. Understanding the spatiotemporal dynamics of LDD is crucial for guiding ecological restoration in the context of rapid environmental and socioeconomic changes. Mutation years were first identified by applying the Mann-Kendall test (MK) to the area time series of the three dominant land-use types with the largest proportional coverage in each climatic zone, and were then used to define the study sub-periods (Stage 1 and Stage 2) from 2000 to 2020. Change vector analysis (CVA), based on GPP and NEP change vectors, and a generalized linear model (GLM) were used to quantify the spatiotemporal patterns of LDD and its driving mechanisms across China’s seven major climatic regions; in the CVA framework, vector magnitude represents the intensity of ecological change, whereas vector direction distinguishes development from degradation. The MK results indicate that a mutation year around 2010 is detected in most climatic regions. CVA results show persistent degradation in the arid northwestern deserts, stage-dependent recovery in Inner Mongolia grasslands, stable forest-cropland contrasts in Northeast China, and widespread post-2010 degradation in North China. Additionally, the analysis uncovers mixed development-degradation mosaics in subtropical regions and increasing fragmentation in tropical coastal zones, showcasing the regional variability of LDD patterns. The results of GLM showed that natural factors predominantly drive LDD except construction land in Stage 1, while anthropogenic factors become more influential in cropland and construction land in Stage 2. GLM results showed clear stage-dependent shifts in the relative importance of natural and anthropogenic drivers, while additional GeoDetector analysis further indicated that interactions among selected drivers often explained more spatial heterogeneity than single factors alone. Overall, this study proposes climate-zone-specific restoration pathways by explicitly linking LDD spatiotemporal patterns to the coupled effects of climate constraints and human pressures. This study provides an integrated scientific foundation for restoration prioritization in China’s terrestrial ecosystems. • The MK test was used to decide the mutation year in different China’s climate zones. • A double-evaluation system was devised to monitor LDD in China. • The GLM was used to decide the driving force of LDD in different climate zones. • Northwest deserts degraded persistently while coastal and southern hills improved. • Nature and human factors dominated LDD in Stage 1 and Stage 2, respectively.

Authors

Institutions

Publication Details

Journal
Environmental Development
Published
2026-04-22
DOI
https://doi.org/10.1016/j.envdev.2026.101479
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spatiotemporal patterns and drivers of land degradation and development in China's terrestrial ecosystems from 2000 to 2020

Wenyin Wu, Chunhui He, Peihong Song, Longfei Huang et al.
Environmental Development
Land Use and Ecosystem Services
article

Spatiotemporal patterns and drivers of land degradation and development in China's terrestrial ecosystems from 2000 to 2020

Wenyin Wu, Chunhui He, Peihong Song, Longfei Huang, Jie Zhang, Wenjie Liu, Kai Zhao
article en

Abstract

ABSTRACT : Land degradation and development (LDD) result from the combined driving force of natural constraints and human activities on the productivity and stability of terrestrial ecosystems. Understanding the spatiotemporal dynamics of LDD is crucial for guiding ecological restoration in the context of rapid environmental and socioeconomic changes. Mutation years were first identified by applying the Mann-Kendall test (MK) to the area time series of the three dominant land-use types with the largest proportional coverage in each climatic zone, and were then used to define the study sub-periods (Stage 1 and Stage 2) from 2000 to 2020. Change vector analysis (CVA), based on GPP and NEP change vectors, and a generalized linear model (GLM) were used to quantify the spatiotemporal patterns of LDD and its driving mechanisms across China’s seven major climatic regions; in the CVA framework, vector magnitude represents the intensity of ecological change, whereas vector direction distinguishes development from degradation. The MK results indicate that a mutation year around 2010 is detected in most climatic regions. CVA results show persistent degradation in the arid northwestern deserts, stage-dependent recovery in Inner Mongolia grasslands, stable forest-cropland contrasts in Northeast China, and widespread post-2010 degradation in North China. Additionally, the analysis uncovers mixed development-degradation mosaics in subtropical regions and increasing fragmentation in tropical coastal zones, showcasing the regional variability of LDD patterns. The results of GLM showed that natural factors predominantly drive LDD except construction land in Stage 1, while anthropogenic factors become more influential in cropland and construction land in Stage 2. GLM results showed clear stage-dependent shifts in the relative importance of natural and anthropogenic drivers, while additional GeoDetector analysis further indicated that interactions among selected drivers often explained more spatial heterogeneity than single factors alone. Overall, this study proposes climate-zone-specific restoration pathways by explicitly linking LDD spatiotemporal patterns to the coupled effects of climate constraints and human pressures. This study provides an integrated scientific foundation for restoration prioritization in China’s terrestrial ecosystems. • The MK test was used to decide the mutation year in different China’s climate zones. • A double-evaluation system was devised to monitor LDD in China. • The GLM was used to decide the driving force of LDD in different climate zones. • Northwest deserts degraded persistently while coastal and southern hills improved. • Nature and human factors dominated LDD in Stage 1 and Stage 2, respectively.

Environmental DevelopmentVol. 59
Hainan University (CN), China International Science and Technology Cooperation (CN), Sanya University (CN), Anqing Normal University (CN)
National Natural Science Foundation of China
Climate action
Openalex Percentile: Top 15%
Land Use and Ecosystem Services
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.